AUTOMATIC PARKINSONS DISEASE DETECTION BASED ON THE COMBINATION OF LONG-TERM ACOUSTIC FEATURES AND MEL FREQUENCY COEFFICIENTS (MFCCs)

Abstract

A system, method, and non-transitory computer readable medium for discriminating between patients with neurodegenerative disease and healthy patients. The method includes obtaining a first plurality of voice signals from known healthy humans and known neurogenerative diseases humans, extracting long-term acoustic features of the first plurality of voice signals, extracting Mel frequency coefficients (MFCCs) from the first plurality of voice signals, creating a set A of short-term acoustic features based on the MFCCs, performing a backward stepwise selection to create a set B of long-term acoustic features and a set C, where set C includes the features of set B combined with the features of set A, creating a random forest classification model, obtaining a second plurality of voice signals from humans of undetermined health status, and applying the second plurality of voice signals against the random forest classification model to determine which patients are neurodegenerative diseased patients.

1 . A machine-learning method to differentiate between patients with neurodegenerative disease and healthy patients, the method comprising:
 obtaining a first plurality of voice signals from known healthy humans and known neurogenerative diseased humans;   extracting one or more long-term acoustic features of the first plurality of voice signals;   extracting Mel frequency coefficients (MFCCs) from each of the first plurality of voice signals;   creating a set A of short-term acoustic features based on the MFCCs;   performing a backward stepwise selection of the long-term acoustic features to obtain a set B of long-term acoustic features and a set C, set C comprising the set B of long-term acoustic features combined with the set A of short-term acoustic features;   configuring a random forest classification model with the features of set C in order to classify healthy patients and neurodegenerative disease patients;   obtaining a second plurality of voice signals from humans of undetermined health status; and   applying the second plurality of voice signals against the random forest classification model in order to determine which patients in the second plurality of voice signals are healthy patients and which are neurodegenerative disease patients.   
     
     
         2 . The method according to  claim 1 , which further comprises determining an accuracy, a specificity, and a sensitivity of the random forest classification model wherein the accuracy is calculated by: 
       
         
           
             
               
                 TP 
                 + 
                 TN 
               
               
                 TP 
                 + 
                 TN 
                 + 
                 FP 
                 + 
                 FN 
               
             
           
         
         the specificity is calculated by: 
       
       
         
           
             
               TN 
               
                 TN 
                 + 
                 FP 
               
             
           
         
         and the sensitivity is calculated by 
       
       
         
           
             
               TP 
               
                 TP 
                 + 
                 FN 
               
             
           
         
         where TP is true positive (TP) indicates a number of correctly classified diseased patients and true negative (TN) expresses a number of correctly classified healthy patients and false positive (FP) indicates a number of incorrectly classified healthy subjects, and false negative (FN) expresses a number of incorrectly classified diseased patients. 
       
     
     
         3 . The method according to  claim 1 , wherein the random forest classification model is created by:
 dividing the first plurality of voice signals into a training set and a test set of voice signals;   building the random forest classification model using bootstrap sampling of the training set wherein the model comprises multiple decision trees produced by multiple training subsets; and   testing the model with the test set of voice signals.   
     
     
         4 . The method of  claim 1 , wherein the Mel frequency coefficients are extracted by a method comprising:
 dividing the voice signals into overlapping frames, each frame containing a plurality of samples wherein the overlap is between 30% and 50% of the frame;   windowing the overlapping frames wherein the window is of length 20-40 ms;   applying a Fast Fourier Transform (FFT) to convert the voice signal to a frequency domain;   calculating logarithm of an average value of a spectral power density in each of the frames to model the voice signal in a cepstral domain;   creating Mel filterbanks within the cepstral domain; and   performing a discrete cosine transformation (DCT) on the Mel filterbanks to determine the Mel frequency coefficients.   
     
     
         5 . The method of  claim 1 , wherein the backward stepwise selection of the long-term acoustic features comprises:
 starting with a model with a full set of long-term acoustic features;   iteratively removing a particular feature that has the least significance for model accuracy;   removing the particular feature from the model when removal of the particular feature from the model improves model performance wherein performance is measured by accuracy, specificity, sensitivity, or an area under a curve;   returning the particular feature to the model when removing the particular feature worsens the model performance; and   repeating removal of each feature in the set of long-term acoustic features until the best performance of the model is achieved as measured by accuracy, a specificity, a sensitivity, or an area under the curve.   
     
     
         6 . The method of  claim 1 , wherein the long-term acoustic features comprise any of:
 a relative average perturbation;   a jitter;   an amplitude perturbation quotient;   a shimmer;   a detrended fluctuation analysis;   a minimum intensity;   a maximum intensity;   a mean intensity; and   a formant frequency.   
     
     
         7 . The method of  claim 1  wherein the neurodegenerative disease is Parkinson's disease. 
     
     
         8 . A medical diagnostic system, comprising:
 one or more processors,   a memory,   a microphone, and   a circuitry configured to:   obtain a first plurality of voice signals from known healthy humans and known neurogenerative diseases humans;   extract one or more long-term acoustic features of the first plurality of voice signals;   extract Mel frequency coefficients (MFCCs) from the first plurality of voice signals;   create a set A of short-term acoustic features based on the MFCCs;   perform a backward stepwise selection of the long-term acoustic features to create a set B of long-term acoustic features and a set C, set C comprising the set B of long-term acoustic features combined with the set A of short-term acoustic features;   configuring a random forest classification model with set the features of set C in order to classify healthy patients and neurodegenerative diseased patients;   obtain a second plurality of voice signals from humans of undetermined health status; and   apply the second plurality of voice signals against the model in order to determine which patients in the second plurality of voice signals samples are healthy patients and which are neurodegenerative diseased patients.   
     
     
         9 . The medical diagnostic system of  claim 8 , wherein the circuitry is additionally configured to determine an accuracy, a specificity, and a sensitivity of the random forest classification model wherein the accuracy is calculated by: 
       
         
           
             
               
                 TP 
                 + 
                 TN 
               
               
                 TP 
                 + 
                 TN 
                 + 
                 FP 
                 + 
                 FN 
               
             
           
         
         the specificity is calculated by: 
       
       
         
           
             
               TN 
               
                 TN 
                 + 
                 FP 
               
             
           
         
         and the sensitivity is calculated by 
       
       
         
           
             
               TP 
               
                 TP 
                 + 
                 FN 
               
             
           
         
       
       where TP is true positive (TP) indicates a number of correctly classified diseased patients and true negative (TN) expresses a number of correctly classified healthy patients and false positive (FP) indicates a number of incorrectly classified healthy subjects, and false negative (FN) expresses a number of incorrectly classified diseased patients. 
     
     
         10 . The medical diagnostic system of  claim 8 , wherein the random forest classification model is created by:
 dividing the first plurality of voice signals into a training set and a test set of voice signals;   using bootstrap sampling of the training set wherein the model comprises multiple decision trees produced by multiple training subsets; and   testing the random forest classification model with the test set of voice signals.   
     
     
         11 . The medical diagnostic system of  claim 8 , wherein the circuitry is configured to extract the MFCCs by:
 dividing the voice signal into overlapping frames, each frame containing a plurality of samples wherein the overlap is between 30% and 50% of the frame;   windowing the overlapping frames where the window is 20-40 ms;   applying a Fast Fourier Transform (FFT) to convert the voice signal to a frequency domain;   calculating logarithms of average values of a spectral power density in each of the frames to model the voice signal in a cepstral domain;   creating Mel filterbanks within the cepstral domain; and   performing a discrete cosine transformation (DCT) on the Mel filterbanks to calculate the MFCCs.   
     
     
         12 . The medical diagnostic system of  claim 8 , wherein the circuitry is configured to calculate the backward stepwise selection of the long-term acoustic features by:
 starting with a model with a full set of long-term acoustic features;   iteratively removing a particular feature that has the least significance for model accuracy;   determining if a removal of a particular feature resulted in improving model performance wherein performance is measured by an accuracy, a specificity, a sensitivity, or an area under a curve, and removing the particular feature from the model;   determining if a removal of the particular feature resulted in worsening the model's performance and returning the particular feature to the model; and   repeating a removal of each feature in the set until the best model accuracy is found.   
     
     
         13 . The medical diagnostic system of  claim 8 , wherein the long-term acoustic features comprise any of:
 a relative average perturbation;   a jitter;   an amplitude perturbation quotient;   a shimmer;   a detrended fluctuation analysis;   a minimum intensity;   a maximum intensity;   a mean intensity; and   a formant frequency.   
     
     
         14 . The medical diagnostic system of  claim 8 , wherein the neurodegenerative disease is Parkinson's disease. 
     
     
         15 . A non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to:
 obtain a first plurality of voice signals from human patients;   extract one or more long-term acoustic features of the voice signals;   extract Mel frequency coefficients (MFCCs) from the voice signals;   create a set A of short-term acoustic features based on the MFCCs;   perform a backward stepwise selection of long-term acoustic features to create a set B of long term acoustic features and a set C, set C comprising long-term acoustic features combined with the set A of short-term acoustic features;   configure a random forest classification model with the features of set C in order to create a classification of healthy patients and neurodegenerative diseased patients;   obtain a second plurality of voice signals; and   apply the second plurality of voice signals against the model in order to determine which of the second plurality of voice signals are from healthy patients and which are from neurodegenerative diseased patients.   
     
     
         16 . The non-transitory computer-readable storage medium according to  claim 15 , wherein the computer-readable instructions further calculate an accuracy, a specificity, and a sensitivity of the random forest classification model wherein the accuracy is calculated by: 
       
         
           
             
               
                 TP 
                 + 
                 TN 
               
               
                 TP 
                 + 
                 TN 
                 + 
                 FP 
                 + 
                 FN 
               
             
           
         
         the specificity is calculated by: 
       
       
         
           
             
               TN 
               
                 TN 
                 + 
                 FP 
               
             
           
         
         and the sensitivity is calculated by 
       
       
         
           
             
               TP 
               
                 TP 
                 + 
                 FN 
               
             
           
         
         where TP is true positive (TP) indicates a number of correctly classified diseased patients and true negative (TN) expresses a number of correctly classified healthy patients and false positive (FP) indicates a number of incorrectly classified healthy subjects, and false negative (FN) expresses a number of incorrectly classified diseased patients. 
       
     
     
         17 . The non-transitory computer-readable storage medium according to  claim 15 , wherein the computer-readable instructions to create a random forest classification model comprise:
 dividing the first plurality of voice signals into a training set and a test set of voice signals;   using bootstrap sampling of the training set wherein the model comprises multiple decision trees produced by multiple training subsets; and   testing the model with the test set of voice signals.   
     
     
         18 . The non-transitory computer-readable storage medium according to  claim 15 , wherein the computer-readable instructions to extract a Mel frequency coefficient comprise:
 framing the voice signal into overlapping frames, each frame containing a plurality of samples wherein the overlap is between 30% and 50% of the frame;   windowing the overlapping frames where the window is 20-40 ms;   applying a Fast Fourier Transform (FFT) to convert the voice signal to a frequency domain;   calculating logarithms of average values of a spectral power density in each of the frames to model the voice signal in a cepstral domain;   creating Mel filterbanks within the cepstral domain; and   performing a discrete cosine transformation (DCT) on the Mel filterbanks to calculate the MFCC.   
     
     
         19 . The non-transitory computer-readable storage medium according to  claim 15 , wherein long-term acoustic features comprise any of:
 a relative average perturbation;   a jitter;   an amplitude perturbation quotient;   a shimmer;   a detrended fluctuation analysis;   a minimum intensity;   a maximum intensity;   a mean intensity; and   a formant frequency.   
     
     
         20 . The non-transitory computer-readable storage medium according to  claim 15 , wherein the neurodegenerative disease is Parkinson's disease.